Recent advancements in the field of image processing have led to development of various methods and techniques for automatic license plate recognition (ALPR). In conventional automatic license plate recognition (ALPR) systems or devices, a camera may capture an image/video which includes a number of vehicles. Typically, in the conventional systems, an optical character recognition (OCR) technique is applied on each license plate of each vehicle in the captured image, to extract a number of license plate numbers from the captured image. Thereafter, the extracted license plate numbers are matched with license plate numbers in received hotlist. This is very time consuming and error-prone. Further, in certain scenarios, the captured image may include a large number of license plates. In such scenarios, application of the OCR technique on each of the large number of license plates, may be a computationally resource intensive process. Further, in the case where a device executing the ALPR is an embedded device, for example, an embedded in-vehicle device in a police car, such conventional techniques may adversely affect the total cycle time and response time of the ALPR process. As a consequence, a suspect vehicle (in the hotlist) may not be detected with certain time, and may escape undetected. In other scenarios, the license plates of the vehicles in the captured image/video, may be tilted, skewed, blurred or pixelated as the angle, speed, and distance of different vehicles with respect to the position of the camera may be different. In such scenarios, conventional ALPR methods and systems may be inefficient, error-prone, and may even fail to extract correct sequence of license plates numbers.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.